Wright Statistical Methods and the Improvement of Data Quality

1. Auflage 2014
ISBN: 978-1-4832-6747-0
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 378 Seiten, Web PDF

Statistical Methods and the Improvement of Data Quality contains the proceedings of The Small Conference on the Improvement of the Quality of Data Collected by Data Collection Systems, held on November 11-12, 1982, in Oak Ridge, Tennessee. The conference provided a forum for discussing the use of statistical methods to improve data quality, with emphasis on the problems of data collection systems and how to handle them using state-of-the-art techniques. Comprised of 16 chapters, this volume begins with an overview of some of the limitations of surveys, followed by an annotated bibliography on frames from which the probability sample is selected. The reader is then introduced to sample designs and methods for collecting data over space and time; response effects to behavior and attitude questions; and how to develop and use error profiles. Subsequent chapters focus on principles and methods for handling outliers in data sets; influence functions, outlier detection, and data editing; and application of pattern recognition techniques to data analysis. The use of exploratory data analysis as an aid in modeling and statistical forecasting is also described. This monograph is likely to be of primary benefit to students taking a general course in survey sampling techniques, and to individuals and groups who deal with large data collection systems and are constantly seeking ways to improve the overall quality of their data.

Autoren/Hrsg.

Wright, Tommy

Inhaltsverzeichnis

1;Front Cover;1
2;Statistical Methods and the Improvement of Data Quality;4
3;Copyright Page;5
4;Table of Contents;6
5;Contributors;12
6;Preface;14
7;Acknowledgments;18
8;Chapter 1. Errors and Other Limitations of Surveys;22
8.1;1. Introduction;22
8.2;2. The Data Collection System Considered: A Survey;23
8.3;FIRST PART - THE LIMITATIONS;25
8.3.1;3. An Inherent Limitation;25
8.3.2;4. Errors in Surveys;29
8.4;SECOND PART - COMPREHENSIVE QUALITY CONTROL;32
8.4.1;5. The Formal Basis for Comprehensive Quality Control;32
8.4.2;6. Quality Control of the Survey Design;33
8.4.3;7. Quality Control of the Survey Operations;36
8.4.4;References;43
9;Chapter 2. A Frame on Frames: An Annotated Bibliography;46
9.1;1. Introduction and Definition of a Frame;46
9.2;2. Construction of the Sampling Frame;48
9.3;3. Area Frames;51
9.4;4. Imperfect Frames;52
9.5;5. Sampling from Imperfect List Frames;58
9.6;6. Multiple Frame Surveys;62
9.7;The Annotated Bibliography;67
10;Chapter 3. Data Collection for Details over Space and Time;94
10.1;1. Introduction;94
10.2;2. Samples and Censuses;95
10.3;3. Samples Connected with Censuses;96
10.4;4. Data from Registers;97
10.5;5. A Brief Historical Overview of Censuses;98
10.6;6. Postcensal Estimates for Domains;98
10.7;7. Design and Estimation for Domains;100
10.8;8. Purposes and Designs for Periodic Samples;101
10.9;9. Rolling Samples;102
10.10;10. Panels with Rolling Samples;103
10.11;11. Summary;104
10.12;References;105
11;Chapter 4. Response Effects to Behavior and Attitude Questions;106
11.1;1. Introduction;106
11.2;2. Non-Threatening Behavior Questions;107
11.3;3. Reducing Memory Error;111
11.4;4. The Length of Questions;119
11.5;5. Threatening Behavioral Questions;120
11.6;6. The Magnitude of Errors in Threatening Questions;121
11.7;7. Determining the Perceived Level of Threat;121
11.8;8. Methods for Improving the Quality of Reporting of Threatening Questions;123
11.9;9. The Use of Familiar Words;124
11.10;10. Deliberately Loading the Question;126
11.11;11. Time Frame for Socially Undesirable and Desirable Behavior;129
11.12;12. Non-Interview Methods;130
11.13;13. Attitudinal Questions;132
11.14;14. Summary;135
11.15;References;135
12;Chapter 5. Error Profiles: Uses and Abuses;138
12.1;1. Introduction;138
12.2;2. How to Develop Error Profiles;140
12.3;3. How to Use Error Profiles;148
12.4;References;150
13;Chapter 6. Principles and Methods for Handling Outliers in Data Sets;152
13.1;1. Introduction;152
13.2;2. Some Basic Considerations for Univariate Samples;153
13.3;3. Multivariate Outliers;165
13.4;4. Outliers in Linear Models;173
13.5;5. Implications of the Data Collecting Mechanism for the Processing of Outliers;180
13.6;References;181
14;Chapter 7. Influence Functions, Outlier Detection, and Data Editing;188
14.1;1. Introduction;188
14.2;2. Multivariate Outliers;188
14.3;3. Influence Functions;190
14.4;4. Form 4 Application;193
14.5;5. Inventory Difference Data Application;194
14.6;6. The Use of Influence Functions for Imputation;195
14.7;References;196
15;Chapter 8. Using Exploratory Data Analysis to Monitor Socio-Economic Data Quality in Developing Countries;198
15.1;1. Introduction;198
15.2;2. The Problem;198
15.3;3. Exploratory Data Analysis;200
15.4;4. Conclusion;211
15.5;References;212
16;Chapter 9. Application of Pattern Recognition Techniques to Data Analysis;214
16.1;1. Introduction;214
16.2;2. Heuristic Approach;216
16.3;3. Decision-Theoretic Approach;217
16.4;4. Syntactic Approach;220
16.5;5. Summary;224
16.6;References;224
17;Chapter 10. Can Automatic Data Editing be Justified? One Person's Opinion;226
17.1;1. Introduction;226
17.2;2. The General Editing Procedure;226
17.3;3. Automatic Data Editing;227
17.4;4. Justifiability of Automatic Data Editing;228
17.5;5. Directions for Further Research;230
17.6;References;230
18;Chapter 11. Missing Data in Large Data Sets;236
18.1;1. Introduction;236
18.2;2. Common Incomplete Data Problems;236
18.3;3. Methods for Handling Incomplete Data;243
18.4;4. General Data Patterns: The EM Algorithm;251
18.5;5. Multiple Imputation;259
18.6;6. Conclusion;260
18.7;References;261
19;Chapter 12. Reducing the Cost of Studying Survey Measurement Error: Is a Laboratory Approach the Answer?;266
19.1;1. Introduction;266
19.2;2. Measurement Error Models;268
19.3;3. The Pearson Laboratory Experiment;269
19.4;4. Considerations for the Laboratory Experiment Approach;271
19.5;5. Summary;283
19.6;References;283
20;Chapter 13. The Implication of Sample Design on Survey Data Analysis;288
20.1;1. Introduction;288
20.2;2. Types of Data Collection Systems;289
20.3;3. Sample Design Effects on Finite Population Inference;291
20.4;4. Examples of Survey Data Analysis and Effects of Design;302
20.5;References;316
21;Chapter 14. An Approach to an Evaluation of the Quality of Motor Gasoline Prices;318
21.1;1. Introduction;318
21.2;2. Background;319
21.3;3. Internal Assessment;322
21.4;4. External Comparisons;334
21.5;5. Summary of Findings;337
21.6;6. General Conclusions Relative to the Quality of Data Collection Systems;338
21.7;References;340
22;Chapter 15. Health and Mortality Study Error Detection, Reporting, and Resolution System;342
22.1;1. Overview;342
22.2;2. Types of Errors Reported;344
22.3;3. System Description;345
22.4;4. Summary;352
22.5;References;353
23;Chapter 16. On Using Exploratory Data Analysis as an Aid in Modeling and Statistical Forecasting;354
23.1;1. Introduction;354
23.2;2. Overview;357
23.3;3. The Wharton Assessment of STIFS;359
23.4;4. The GLOBUS Project;367
23.5;5. Concluding Remarks;370
23.6;References;371
24;Index;376